{"cells":[{"cell_type":"code","source":["%%capture\n","!pip install transformers==4.29.0  peft==0.3.0 datasets==2.12.0"],"metadata":{"id":"onTs33D8r8x2","executionInfo":{"status":"ok","timestamp":1694434695556,"user_tz":-180,"elapsed":12835,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"id":"onTs33D8r8x2","execution_count":1,"outputs":[]},{"cell_type":"markdown","source":["# New Section"],"metadata":{"id":"yKcEjDDpoKJ1"},"id":"yKcEjDDpoKJ1"},{"cell_type":"code","source":["!pip list |egrep transformers\n","!pip list |egrep peft\n","!pip list |egrep ^datasets\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nXFwrtm_TABK","executionInfo":{"status":"ok","timestamp":1694434700778,"user_tz":-180,"elapsed":3179,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"15e10122-3835-47b7-a4a9-f06f593bef38"},"id":"nXFwrtm_TABK","execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["transformers                     4.29.0\n","peft                             0.3.0\n","datasets                         2.12.0\n"]}]},{"cell_type":"code","execution_count":3,"id":"5f93b7d1","metadata":{"id":"5f93b7d1","executionInfo":{"status":"ok","timestamp":1694434710400,"user_tz":-180,"elapsed":8498,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"outputs":[],"source":["from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n","from transformers import (default_data_collator,\n","                          get_linear_schedule_with_warmup)\n","from peft import (get_peft_config,\n","                  get_peft_model,\n","                  get_peft_model_state_dict,\n","                  LoraConfig,\n","                  TaskType)\n","import torch, os\n","from torch.utils.data import DataLoader\n","from tqdm import tqdm\n","import pandas as pd\n","from datasets import (load_dataset,\n","                      Dataset, DatasetDict)"]},{"cell_type":"markdown","source":["# Loading SNLI dataset from huggingface dataset hub"],"metadata":{"id":"lZCKJEpMMURo"},"id":"lZCKJEpMMURo"},{"cell_type":"code","source":["dataset = load_dataset(\"snli\")\n","snli_sampled=pd.DataFrame(dataset[\"train\"])\n","snli_sampled=snli_sampled.sample(frac=0.01, random_state=123)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":329,"referenced_widgets":["760c029ad34f4d3fb417bd775b2db383","53f0ac10cecf421ea72130365615cd71","4ecc2a87251a43fd9dc55f46a124f2f2","3f802f2c635e49e9bf10fbff0291f736","e6dc912d7a8b461bb73d0174a89b4d2a","60d4488e751b48ba822235d91feb5dc4","e9d992602e214faea7436a2e16dd848a","3eaba762f6ce4358a78943df38188f3a","1bc2fa304a6d4a1291844197523000c1","dd27e0e7a65743f991ab91438ca0f3b7","9d2ae749f1e7445f977850435f7a0f78","fcd2a4a8897f447da75d343b04c43d92","0e8010d1fb254cef97180d74164bdaf6","f0faff1481b14791b6dfe73769ae452b","1769bbc5c2a54b38ba40283d7e7a0a8b","9d9e2d7eaf40476491f4c944621c2090","efd9dd921fc342239b2c92da7bd1c005","a89167a81f624a23a2e1c68971287e79","7bd03e8a672c4aff8e988316ec44ab25","71a3bead94d1460692991bdd862b28ba","c7648168ef584f5397350930d0437ea9","2dfcc1b7104444d3b98c6f5f618f0cd9","2d4e044dba624592a493ca8ca2471c25","9a813199ae13423e931fcd79e144ce60","a45d869a6653404390bc62658b8d9433","86d3fe0a0f8d4f279f61dae78a6254fa","12c2641c08d64e32b6ad3e28f99e4e53","f2ad605df167467da5029860078b0b1a","fb11766740344241b55ef896b4975c05","e2b71c0ca4214065bacb4424cf50136a","7b4e44876e7f43b2bea82aa0edff3be3","86a82dd2a1754aa18863a93d36e94ab9","1edfd0faef654770b7bb9e94c632af2c","2cc2f9a1d045453bbd2ec47dc4d76a00","36228a700bc4474fb59b1d26ff56feea","613094a847454148a2df7b75fb1ddd1c","dbde20e230f84ff893ced76f3ab049d7","e7e6ef7ee6f3447d90095d392ae1c15e","08481766f397433f82c970090d041045","9b2d78a3d2d34df88036499b2de16984","c00415e26da14af88f366a5eb6669044","292a7925ac8744e78b735fac4b8648ea","ee41e6e6aaa145d38c90155c570793ba","e7d22b18dee64917b5638d0e7f3e3b0c","c6a0af42380c4e9080f6cfde8782149b","a6a305696c1e4b9a8eee4fcaead6032b","4d4b970bd7664c669c691bcc3f56b089","6ce500fd56ae468b994ca6eda236d821","8b7eb6c4113f4eac85456aeeffe6afff","b886c542a0ad4e1fad5b625d2a48c67a","2f04e6749984497d8eff2e8c7a01c5b6","e797b10d10d64544a6f1beb6c3cccb8a","cd98e872d239403aadc10e8f42c97506","5526528e555f49bdbcd78b90e18bc59d","5cf445db0509467f902de76dce3fb476","9b499e67d3494f449e5c0a5dce102c06","de65bb9409de454694dce15b6d00a4fe","f328d407795c41e0b0df3967930bc7bc","cdbb6d80b70d4764a8caa21cbaee372e","01b1741b2d214ed4a10bf0aee72f7dd9","1e959be893254636b565a15ff5df47a7","b9dff15dfe27416e8dedf6819e517e1d","5b4fa767e1374d119a41283c4b4dfbee","54dc6487f0f548c0b65e85b499441bcb","bf16bc309cb743d3ac7a089442e300c5","d3252024df0f49c1a87465f12e4d1e4e","521925bfedcf444eaf6957052b2c6294","6ac43d3b245c4b20a9e210c0876d4e42","0fc099a3d3564db198ea273621fe3409","d292b54265b04c15acf81dd5610e3522","7990c2683a394da4b9c03f9bec589410","360f570d50df48c7bcf12a974b92bf8c","d41e5b5949704a8191eeb9bb9b89d921","7358ce7178e741b08033585d7b68a511","6a203975a5cf498796836c1da46f74bf","1f7bf41bf5504c03947966dca1a33c4d","6418c51b631745d6972de28fde2c892f","7c33e7d20c584ce4bb1ba2e41f80b15a","08a21be4034a4336a7b7929018e64a44","f9bfac9b459f44b5a04ca2d327d4469d","38ceefbc18f345659561be89daca2a66","96d7039087a045af8919a65dfe8dd26f","d719d85ab6e84c36966b4ac0941328ef","fd8df13cfdb549748bbd0bc496ba93c3","1c8906135b6b4017b9ddb16d62edd684","0f597d43ef544e3e8c366372043c7c39","6c10cfb6c91446348bb0e66f27468890","1718bc5ccfbe40f6b50dbd34b2c5d19a"]},"id":"GLCU6GVN-pBC","executionInfo":{"status":"ok","timestamp":1694434730139,"user_tz":-180,"elapsed":19743,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"1f24190f-cf58-4d3a-b79c-7e3fbd6a6cde"},"id":"GLCU6GVN-pBC","execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/3.82k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"760c029ad34f4d3fb417bd775b2db383"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading metadata:   0%|          | 0.00/1.90k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fcd2a4a8897f447da75d343b04c43d92"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/14.1k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2d4e044dba624592a493ca8ca2471c25"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset snli/plain_text to /root/.cache/huggingface/datasets/snli/plain_text/1.0.0/1f60b67533b65ae0275561ff7828aad5ee4282d0e6f844fd148d05d3c6ea251b...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.93k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2cc2f9a1d045453bbd2ec47dc4d76a00"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.26M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c6a0af42380c4e9080f6cfde8782149b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/65.9M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9b499e67d3494f449e5c0a5dce102c06"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.26M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"521925bfedcf444eaf6957052b2c6294"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset snli downloaded and prepared to /root/.cache/huggingface/datasets/snli/plain_text/1.0.0/1f60b67533b65ae0275561ff7828aad5ee4282d0e6f844fd148d05d3c6ea251b. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7c33e7d20c584ce4bb1ba2e41f80b15a"}},"metadata":{}}]},{"cell_type":"code","source":["snli_sampled.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rJfgeX6z_Nw1","executionInfo":{"status":"ok","timestamp":1694434730140,"user_tz":-180,"elapsed":45,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"8222e967-2516-498b-d423-8a54a6d73504"},"id":"rJfgeX6z_Nw1","execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(5502, 3)"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["snli_sampled.label.value_counts()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4A8do1hj-mcs","executionInfo":{"status":"ok","timestamp":1694434730141,"user_tz":-180,"elapsed":38,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"61ea0df1-a716-4fdf-a7e6-49aca23c6875"},"id":"4A8do1hj-mcs","execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" 2    1866\n"," 0    1828\n"," 1    1803\n","-1       5\n","Name: label, dtype: int64"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["snli_sampled= snli_sampled[snli_sampled.label>-1]"],"metadata":{"id":"laoIiiLp_TBk","executionInfo":{"status":"ok","timestamp":1694434730142,"user_tz":-180,"elapsed":32,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"id":"laoIiiLp_TBk","execution_count":7,"outputs":[]},{"cell_type":"code","source":["names=dataset[\"train\"].features[\"label\"].names\n","names"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0DKTCDX6_S_B","executionInfo":{"status":"ok","timestamp":1694434730142,"user_tz":-180,"elapsed":30,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"aa0aa5c1-c575-4b9d-9e68-5904ce207891"},"id":"0DKTCDX6_S_B","execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['entailment', 'neutral', 'contradiction']"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["mapp=dict(enumerate(names))\n","mapp"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UDEVPadF_S8Z","executionInfo":{"status":"ok","timestamp":1694434730143,"user_tz":-180,"elapsed":26,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"e96bdb31-64d4-4eff-fac7-63add554d064"},"id":"UDEVPadF_S8Z","execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{0: 'entailment', 1: 'neutral', 2: 'contradiction'}"]},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","source":["## Preparing dataset for training"],"metadata":{"id":"SwXwnhgCOF_m"},"id":"SwXwnhgCOF_m"},{"cell_type":"code","source":["snli_sampled_df= pd.DataFrame(snli_sampled)\n","snli_sampled_df[\"text\"]= snli_sampled_df\\\n","      .apply(lambda x: \"S1:\" +x.premise\n","             +\" S2:\"+x.hypothesis+\n","             \". The relation between S1 and S2 is labeled \"+\n","             \"as entailment, neutral or contradiction ?\",\n","            axis=1)\n","snli_sampled_df[\"label\"]=snli_sampled_df\\\n","        .apply(lambda x: f\"It is {mapp[x.label]}\",\n","               axis=1)"],"metadata":{"id":"dellBc6o_S58","executionInfo":{"status":"ok","timestamp":1694434730143,"user_tz":-180,"elapsed":23,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"id":"dellBc6o_S58","execution_count":10,"outputs":[]},{"cell_type":"code","source":["snli_sampled_df.head(3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"YiYgx9CAFMCj","executionInfo":{"status":"ok","timestamp":1694434730144,"user_tz":-180,"elapsed":23,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"0c960201-cf96-4f45-c198-2d093fec353a"},"id":"YiYgx9CAFMCj","execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                  premise  \\\n","190268  Two firefighters clad in protective gear are e...   \n","300525   Two men work together on a construction project.   \n","465851             Three men in uniform walk around town.   \n","\n","                                    hypothesis                label  \\\n","190268  Two firefighters are entering a house.     It is entailment   \n","300525                    Two men are working.     It is entailment   \n","465851            Three men rob the residents.  It is contradiction   \n","\n","                                                     text  \n","190268  S1:Two firefighters clad in protective gear ar...  \n","300525  S1:Two men work together on a construction pro...  \n","465851  S1:Three men in uniform walk around town. S2:T...  "],"text/html":["\n","  <div id=\"df-a4bed77b-0062-43e1-9412-246ea9dc4f44\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>premise</th>\n","      <th>hypothesis</th>\n","      <th>label</th>\n","      <th>text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>190268</th>\n","      <td>Two firefighters clad in protective gear are e...</td>\n","      <td>Two firefighters are entering a house.</td>\n","      <td>It is entailment</td>\n","      <td>S1:Two firefighters clad in protective gear ar...</td>\n","    </tr>\n","    <tr>\n","      <th>300525</th>\n","      <td>Two men work together on a construction project.</td>\n","      <td>Two men are working.</td>\n","      <td>It is entailment</td>\n","      <td>S1:Two men work together on a construction pro...</td>\n","    </tr>\n","    <tr>\n","      <th>465851</th>\n","      <td>Three men in uniform walk around town.</td>\n","      <td>Three men rob the residents.</td>\n","      <td>It is contradiction</td>\n","      <td>S1:Three men in uniform walk around town. 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ECC |\n","| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n","|                               |                      |               MIG M. |\n","|===============================+======================+======================|\n","|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   60C    P8    10W /  70W |      0MiB / 15360MiB |      0%      Default |\n","|                               |                      |                  N/A |\n","+-------------------------------+----------------------+----------------------+\n","                                                                               \n","+-----------------------------------------------------------------------------+\n","| Processes:                                                                  |\n","|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n","|        ID   ID                                                   Usage      |\n","|=============================================================================|\n","|  No running processes found                                                 |\n","+-----------------------------------------------------------------------------+\n"]}]},{"cell_type":"code","source":["model_name_or_path=\"google/flan-t5-base\" # 250M parameters\n","#model_name_or_path=\"google/flan-t5-large\" # 780M parameters\n","#model_name_or_path=\"google/flan-t5-xl\" # 3B parameters\n","tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)"],"metadata":{"id":"hFdbH42kkige","colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["a55c6f85a92c497eb9be4eefb8587d2f","da1c87bcb92042f29467d5272d1db073","1016729b8dbf43bf8a31b327f9341857","6007ffd3db5b46e98cf1872edd1f8fb7","68b1802d3bf6424491e7b2c0b576da65","67920be017494733b812f4b35acbf7fd","14dae0be8d88425690282d05a375e07e","55167a5ead904f1ba53477bc612770dd","d080b29e0b144378b5e4880031829f4b","23f10812657742ba88a6d2669bec46d3","9c1dd9ab9285402d9a161d1428c04297","8ae32a189e034d648425428257d94170","2c0f1574fc7945ab9b08c931f2504e51","9104af7f473642e0b00c5e315bb5fcd8","c75824b4fae14a8ab0eba697b07f16c2","bd0a19e019c042c79aea2ffbd20b40b8","a96bf52526d449d699a7c878295e05a2","12c3044062ca4c26b4c23f86ae147200","c8ef38b630da4f0b97e3dc077c43a0fb","9416ae6ac7014d49a810d892a9436713","bd38472bd13f412b8cb61fb1f0af684b","8068b27462294219b8e96cef54d97da9","0f1c24f854244e199df75e5e6c685b9f","864c9da5b1794273bbeadf17b2bbbfbd","c91ef3f86f1c43dcb8f625d28355617e","81f5e218b1404f53b15e840645f939b5","7f5eda59b863487c9f207e496d82d7bc","9fd013a0f93546eba3e2d4f6a4c8d38f","c80978754764477b80a3b6b83a22f65e","01c980524a6443b1a814804bae4210fc","5ea74f99df1e40b69033f7708169bdb6","d31fe6fe6ba84089ba7494ad7cbf9502","d34c675e31c5416b92537010a480a555","63704fae793348fd8b5c82f8f44927aa","7b702117082246499fe79bd83610cc1a","d326b6bd00aa41b68ecf35cd9d0af42b","ca09ac61cd344758846473d58bf9a226","f67bf64448104c4693b4dbcf0c9c40de","47ca718450a045d4a21cd3e18d870e36","97ca3c9f511440cf90cf10797e7a549c","a884e16993be4ef1bc489dc99da14ea2","3b81784c2e7e47598c375c00c298e150","b4ada67e64744f42bcc4ed88ebc32110","2f69f58050954e84a3339f5632f10f24"]},"executionInfo":{"status":"ok","timestamp":1694434731387,"user_tz":-180,"elapsed":1257,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"640238b0-97e7-47ef-9581-8f63428ad06a"},"id":"hFdbH42kkige","execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a55c6f85a92c497eb9be4eefb8587d2f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8ae32a189e034d648425428257d94170"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0f1c24f854244e199df75e5e6c685b9f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"63704fae793348fd8b5c82f8f44927aa"}},"metadata":{}}]},{"cell_type":"code","source":["max_length = 150\n","max_target_len=10\n","\n","snli_processed = snli_sampled_dict.map(\n","    preprocess_function,\n","    batched=True,\n","    num_proc=1,\n","    remove_columns=snli_sampled_dict[\"train\"].column_names,\n","    load_from_cache_file=False,\n","  )\n","train_dataset = snli_processed[\"train\"]\n","eval_dataset = snli_processed[\"validation\"]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":17,"referenced_widgets":["cf93004e1fd04eab972124a831ee4189","47dd74fc807540ce9fb71f800a9abd6b","33c6258b3ab3410aa34cdc6a26bb3b71","fb4755183d2c46ce91b34d121314f509","788538a223184d569f81b0a418da119e","ea7a7a9aa0d84197821ea0611984125c","b76a6ebdbe32490d916dc6e1cfdc43ce","13577dc58fc84e52a63af1365f4533e1","fee0b70ea6a74e9ab764d028fe03f76b","7ba7a482ebe04d25844a0914b93ec2d7","2901eb47ea0a4eca8df03946ad78f068","21e045bcd3dc454e83643e077d2a1f3f","e4363daf75cc4b71a02afe6c01cf36b0","8faa3092b01c4d7e95b2e922158c6328","5405791907c842498636a1c193ee9ea4","c357f18af965476e8e03d6c32de3f4c3","26cbf3dd43fb44ba87bb61ad9296102d","53a7c5f9c08e42b68fc4a99f1dfa0ac7","7fd7cc4c900f45df9182ba2ead144505","8bec44c6f5de475cb1497d6446d5fcb6","ba1c14a5a5d24136b0519c20b3cf7b44","3b308f0612464100906c7ef3b9476685"]},"id":"DXpKZLRB_Svs","executionInfo":{"status":"ok","timestamp":1694434733267,"user_tz":-180,"elapsed":787,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"0955260f-d698-4d1c-cdd1-4f214b75eca5"},"id":"DXpKZLRB_Svs","execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/3847 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cf93004e1fd04eab972124a831ee4189"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/1650 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"21e045bcd3dc454e83643e077d2a1f3f"}},"metadata":{}}]},{"cell_type":"code","source":["pd.DataFrame(train_dataset).head(3)"],"metadata":{"id":"5IbOwEYmGdvv","colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"status":"ok","timestamp":1694434735721,"user_tz":-180,"elapsed":760,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"7726e9f5-5f9b-4876-84fb-53d7ede861cd"},"id":"5IbOwEYmGdvv","execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                           input_ids  \\\n","0  [180, 536, 10, 382, 210, 32, 29764, 3, 4651, 2...   \n","1  [180, 536, 10, 382, 210, 32, 1076, 161, 544, 3...   \n","2  [180, 536, 10, 11889, 15, 15, 1076, 16, 7117, ...   \n","\n","                                      attention_mask  \\\n","0  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n","1  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n","2  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n","\n","                                              labels  \n","0    [94, 19, 3, 35, 5756, 297, 1, -100, -100, -100]  \n","1    [94, 19, 3, 35, 5756, 297, 1, -100, -100, -100]  \n","2  [94, 19, 27252, 1, -100, -100, -100, -100, -10...  "],"text/html":["\n","  <div id=\"df-3e8abd2a-a43d-4963-a8c6-dfb49a9f3db2\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>input_ids</th>\n","      <th>attention_mask</th>\n","      <th>labels</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>[180, 536, 10, 382, 210, 32, 29764, 3, 4651, 2...</td>\n","      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n","      <td>[94, 19, 3, 35, 5756, 297, 1, -100, -100, -100]</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>[180, 536, 10, 382, 210, 32, 1076, 161, 544, 3...</td>\n","      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n","      <td>[94, 19, 3, 35, 5756, 297, 1, -100, -100, -100]</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>[180, 536, 10, 11889, 15, 15, 1076, 16, 7117, ...</td>\n","      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n","      <td>[94, 19, 27252, 1, -100, -100, -100, -100, -10...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e8abd2a-a43d-4963-a8c6-dfb49a9f3db2')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-3e8abd2a-a43d-4963-a8c6-dfb49a9f3db2 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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vanilla mode, set with_peft=False\n","with_peft=True\n","model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)\n","lr=2e-5\n","if with_peft:\n","  lr=1e-3\n","  peft_config = LoraConfig(task_type=TaskType.SEQ_2_SEQ_LM,\n","                           inference_mode=False,\n","                           r=8,\n","                           lora_alpha=32,\n","                           lora_dropout=0.1)\n","  model = get_peft_model(model, peft_config)\n","  model.print_trainable_parameters()"],"metadata":{"id":"UUiNdc6LwwxV","colab":{"base_uri":"https://localhost:8080/","height":131,"referenced_widgets":["091f4e3babfb4f819eb27510bd1b89f8","8d02250c0a3049c7b49ba538221b5c6b","314618cb7bfc4fc1a3c64f661e8e7b51","c4b9743af46a4b33836dc2331d5a1203","ed96482572af452f83b03a74a91ccd35","fc6322b89c2f4f3994c8880e0ebf8e13","305df9e722c94fc698c5cee678beb592","4507165e8fb04765a7f016afb4bdb26e","3baca319a7854ca9952e93008165681e","c12abfc434f249dd8354bf1d8e119481","4d513e4bdf2d4eecb54f4261d8cc1b8d","c114203133ec462db1068dc86a281ec2","0e09b5f5af494f478dbff5d90853f5ae","82ac69e64964444ba88458e3b0057491","9c4233c1da444fc595bc0d6001cb72b5","837eab67fad64bce8f2b1a8aee4c0053","ebe776e2f0f245d5b0c9bb0c56efcfd8","1eae80870a8c4bfc946bd844bed24c72","201e5413a86d4122a3ec078753993a04","41af62a35ac54ce1bce0a79a0266e108","761acf6defe44b5b900580412b696f68","6e37649bb0974ef290b3eb50f8575f9f","04cc5609b1184559ba88de7e6590540b","dfffd396e993457f97bef1fcda33ccd2","63db78ee54044a43bd0df0c51b56dfa6","233950ca6dd945c491482f6b936cca8e","34d570eac90a436db102eb0ae16e8f86","0e513a30e2ec4a8d9f30c811e02f1608","28f983a1ab824dd4a002c6907e929477","35aef57d34844e07a56fe683640e1411","39a8d0a949394ae29df743806a11cb19","4e4cfba0c68540dda058e40603967c34","79168593ac7f47179de5b94ee4140b97"]},"executionInfo":{"status":"ok","timestamp":1694434757808,"user_tz":-180,"elapsed":11270,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"108781c4-2d1c-474e-83ab-a2e581fd2b57"},"id":"UUiNdc6LwwxV","execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/1.40k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"091f4e3babfb4f819eb27510bd1b89f8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/990M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c114203133ec462db1068dc86a281ec2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)neration_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"04cc5609b1184559ba88de7e6590540b"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["trainable params: 884736 || all params: 248462592 || trainable%: 0.3560841867092814\n"]}]},{"cell_type":"code","execution_count":null,"id":"6b3a4090","metadata":{"id":"6b3a4090","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1685265064654,"user_tz":-180,"elapsed":90668,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"94345528-9b77-42ab-bad5-352e2ce0ea8b"},"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 121/121 [00:25<00:00,  4.83it/s]\n","100%|██████████| 52/52 [00:05<00:00, 10.20it/s]\n"]},{"output_type":"stream","name":"stdout","text":["epoch=0-> train_loss_avg=tensor(0.3226, device='cuda:0')\t eval_loss_avg=tensor(0.0877, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 121/121 [00:25<00:00,  4.83it/s]\n","100%|██████████| 52/52 [00:05<00:00, 10.22it/s]\n"]},{"output_type":"stream","name":"stdout","text":["epoch=1-> train_loss_avg=tensor(0.1090, device='cuda:0')\t eval_loss_avg=tensor(0.0846, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 121/121 [00:25<00:00,  4.83it/s]\n","100%|██████████| 52/52 [00:05<00:00, 10.19it/s]"]},{"output_type":"stream","name":"stdout","text":["epoch=2-> train_loss_avg=tensor(0.0981, device='cuda:0')\t eval_loss_avg=tensor(0.0810, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["\n"]}],"source":["device=\"cuda\"\n","model = model.to(device)\n","num_epochs = 3\n","\n","# optimizer and lr scheduler\n","optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n","lr_scheduler = get_linear_schedule_with_warmup(\n","    optimizer=optimizer,\n","    num_warmup_steps=0,\n","    num_training_steps=(len(train_dataloader) * num_epochs),\n",")\n","\n","import time\n","st = time.time()\n","for epoch in range(num_epochs):\n","    model.train()\n","    total_loss = 0\n","    for step, batch in enumerate(tqdm(train_dataloader)):\n","        batch = {k: v.to(device) for k, v in batch.items()}\n","        outputs = model(**batch)\n","        loss = outputs.loss\n","        total_loss += loss.detach().float()\n","        loss.backward()\n","        optimizer.step()\n","        lr_scheduler.step()\n","        optimizer.zero_grad()\n","    model.eval()\n","    eval_loss = 0\n","    eval_preds = []\n","    for step, batch in enumerate(tqdm(eval_dataloader)):\n","        batch = {k: v.to(device) for k, v in batch.items()}\n","        with torch.no_grad():\n","            outputs = model(**batch)\n","        loss = outputs.loss\n","        eval_loss += loss.detach().float()\n","        eval_preds.extend(\n","            tokenizer.batch_decode(\n","                torch.argmax(outputs.logits, -1)\\\n","                .detach().cpu().numpy(),\n","                skip_special_tokens=True)\n","        )\n","    eval_loss_avg = eval_loss / len(eval_dataloader)\n","    train_loss_avg = total_loss / len(train_dataloader)\n","    print(f\"{epoch=}-> {train_loss_avg=}\\t {eval_loss_avg=}\")\n","et = time.time()\n","elapsed_time = et - st"]},{"cell_type":"code","source":["zipped=zip(eval_preds, snli_sampled_dict[\"validation\"][\"label\"])\n","q=[real.strip() in pred.strip() for pred,real in zipped]\n","print(f\"{model_name_or_path=}\")\n","print(f\"{num_epochs=}\")\n","print(f\"{elapsed_time=:.2f} seconds\"\n","     + (\" with PEFT\" if with_peft else  \" without PEFT\"))\n","print(f\"Accuracy:{sum(q)/len(q):.2f}\")"],"metadata":{"id":"fSjt3zxlbZ01"},"id":"fSjt3zxlbZ01","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":[],"metadata":{"id":"FkeK06D3QbtW"},"id":"FkeK06D3QbtW"},{"cell_type":"markdown","source":["# Saving PEFT models"],"metadata":{"id":"wMiysPBaJx9Q"},"id":"wMiysPBaJx9Q"},{"cell_type":"code","execution_count":null,"id":"a8de6005","metadata":{"id":"a8de6005"},"outputs":[],"source":["# saving model\n","peft_model_path=\"my_lora_model\"\n","model.save_pretrained(peft_model_path)"]},{"cell_type":"code","source":["!ls -lh $peft_model_path"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wmYS-0zAPDIZ","executionInfo":{"status":"ok","timestamp":1685267344511,"user_tz":-180,"elapsed":5,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"d298cb76-0fb4-4af4-ded3-3d5f9eafc403"},"id":"wmYS-0zAPDIZ","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["total 3.5M\n","-rw-r--r-- 1 root root  332 May 28 09:49 adapter_config.json\n","-rw-r--r-- 1 root root 3.5M May 28 09:49 adapter_model.bin\n"]}]},{"cell_type":"markdown","source":["# Alternative Saving with merge_and_unload\n","This function merges the adapter weights with the base model, making it easier to use the merged model as a standalone model.\n","\n","\n","\n","```\n","model = model.merge_and_unload()\n","model.save_pretrained(\"my_lora_model_merged\")\n","```\n","\n","\n"],"metadata":{"id":"yHu6i6jZTviW"},"id":"yHu6i6jZTviW"},{"cell_type":"markdown","source":["# Loading Saved PEFT model"],"metadata":{"id":"NtC15V8ER5wb"},"id":"NtC15V8ER5wb"},{"cell_type":"code","execution_count":null,"id":"76c2fc29","metadata":{"id":"76c2fc29","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1685267379389,"user_tz":-180,"elapsed":513,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"436c2383-a49f-4089-e9e2-5496d52d12bd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["PeftConfig(peft_type='LORA', base_model_name_or_path='google/flan-t5-base', task_type='SEQ_2_SEQ_LM', inference_mode=True)"]},"metadata":{},"execution_count":46}],"source":["from peft import PeftModel, PeftConfig\n","config = PeftConfig.from_pretrained(peft_model_path)\n","config"]},{"cell_type":"code","source":["model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)\n","model = PeftModel.from_pretrained(model, peft_model_path)\n","model.eval()"],"metadata":{"id":"0mWGEshUQ_4y"},"id":"0mWGEshUQ_4y","execution_count":null,"outputs":[]},{"cell_type":"code","source":["my_text= snli_sampled_dict[\"validation\"][\"text\"][0]\n","my_label= snli_sampled_dict[\"validation\"][\"label\"][0]\n","print(f\"{my_text=}\")\n","print(f\"{my_label=}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kqizWfRTRVx7","executionInfo":{"status":"ok","timestamp":1685267404981,"user_tz":-180,"elapsed":8,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"607dcecb-e17b-441c-a3f2-2140c2bb42b1"},"id":"kqizWfRTRVx7","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["my_text='S1:The young man wearing a blue sweatshirt and blue jeans is hopping over the railing on the fence. S2:The young man is hopping over the railing to save the young girl from drowning in the pool.. The relation between S1 and S2 is labeled as entailment, neutral or contradiction ?'\n","my_label='It is neutral'\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"Fx7RxxozSU0R"},"id":"Fx7RxxozSU0R","execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"id":"37d712ce","metadata":{"id":"37d712ce","outputId":"769a77de-6950-4313-cd69-50ec188000ea","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1685267449616,"user_tz":-180,"elapsed":507,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': tensor([[  180,   536,    10,   634,  1021,   388,  5119,     3,     9,  1692,\n","         10242,  9486,    11,  1692, 14118,    19,     3, 21714,   147,     8,\n","          6579,    53,    30,     8,  8227,     5,   180,   357,    10,   634,\n","          1021,   388,    19,     3, 21714,   147,     8,  6579,    53,    12,\n","          1097,     8,  1021,  3202,    45, 24614,    53,    16,     8,  2201,\n","             5,     5,    37,  4689,   344,   180,   536,    11,   180,   357,\n","            19,  3783,    15,    26,    38,     3,    35,  5756,   297,     6,\n","          7163,    42, 27252,     3,    58,     1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","         1, 1, 1, 1]])}\n"]}],"source":["inputs = tokenizer(my_text, return_tensors=\"pt\")\n","print(inputs)"]},{"cell_type":"code","source":["with torch.no_grad():\n","    outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n","    print(outputs)\n","    print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8HdajMpARJqU","executionInfo":{"status":"ok","timestamp":1685046175837,"user_tz":-180,"elapsed":596,"user":{"displayName":"Savas Yıldırım","userId":"10717726124681851716"}},"outputId":"bae6430e-f47c-46f8-d519-4d168075524b"},"id":"8HdajMpARJqU","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([[   0,   94,   19, 7163,    1]])\n","['It is neutral']\n"]}]},{"cell_type":"markdown","source":["# Loading merged model\n","\n","If you applied merge_and_unload function, you should call the following code line\n","\n","\n","\n","```\n","model = AutoModelForSeq2SeqLM.from_pretrained(\"my_lora_model_merged\", load_in_8bit=True)\n","```\n","\n"],"metadata":{"id":"E0QS-Kz0UylJ"},"id":"E0QS-Kz0UylJ"},{"cell_type":"markdown","source":["# QLORA\n","To use QLora, we only initialize the model with the following piece of code. The rest of the code is same with the code above! The code needs *bitsandbytes* module. So we start with installing it"],"metadata":{"id":"Y938Epr3oONx"},"id":"Y938Epr3oONx"},{"cell_type":"code","source":["!pip install bitsandbytes"],"metadata":{"id":"tmQ4NF3wog8F"},"id":"tmQ4NF3wog8F","execution_count":null,"outputs":[]},{"cell_type":"code","source":["from transformers import BitsAndBytesConfig\n","from peft import prepare_model_for_kbit_training\n","from transformers import AutoModelForSeq2SeqLM\n","\n","model_name = \"google/flan-t5-base\"\n","\n","\n","bnb_config = BitsAndBytesConfig(\n","    load_in_4bit=True,\n","    bnb_4bit_use_double_quant=True,\n","    bnb_4bit_quant_type=\"nf4\",\n","    bnb_4bit_compute_dtype=torch.bfloat16\n",")\n","\n","model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path,\n","                                              quantization_config=bnb_config,\n","                                              device_map={\"\":0})\n","\n","model.gradient_checkpointing_enable()\n","model = prepare_model_for_kbit_training(model)\n","model = get_peft_model(model, peft_config)"],"metadata":{"id":"f36Zh1S-c_Fu"},"id":"f36Zh1S-c_Fu","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 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